A chemical, biological, or radiological attack on a civilian population is a dreadful event. The best response requires the earliest possible detection of the attack so that individuals can flee and civil defense authorities can contain its effects. To this end, chemical, biological, and radiological (“CBR”) detection systems are being developed for deployment in urban centers.
But accurately detecting the presence of CBR agents that have been released in a public environment is a challenging task. A variety of factors can hamper detection and lead to false alarms. These factors include: background fluctuations in a property being monitored (e.g., particulate size, etc.), the presences of interferants, differing temperature and humidity conditions, low signal-to-noise ratio of a detector, and detector malfunctions, among others.
The public will have little tolerance for false alarms, especially those that result in significant inconvenience, such as the disruption of mass transit facilities during rush hour. If the false alarms were to occur with regularity, a “boy-who-called-wolf” attitude could rapidly develop; that is, the public would soon learn to ignore the alarms.
One way to reduce the incidence of false alarms would be to decrease detector sensitivity. But this is not a workable solution because however inconvenient a false alarm might be, a false-negative indication (i.e., an undetected attack), as might result from intentionally decreasing detector sensitivity, is far worse. Cognizant of this fact, scientists and engineers have addressed this problem in other ways.
One approach to improving the accuracy of CBR detection is to provide systems that incorporate plural detectors that use different analysis methods. The theory is that if multiple sensors that are based on different operating principles all indicate an alarm condition, there is a greater likelihood that the indication is correct than would be the case if the alarm were based on a single analysis method (even if performed by multiple sensors). The reality, however, is that the different analysis methods that are typically used are not truly independent, but rather quasi-independent. And these quasi-independent techniques might be susceptible to the same type of errors for a given set of conditions, thereby undercutting the validity of this approach. Furthermore, these sensors are typically expensive. And incorporating what is, essentially, redundant sensors, increases cost.
Another approach to decreasing false alarms is to use video cameras (e.g., to monitor suspicious activity, etc.) to supplement CBR sensors. Of course, to be of any value, the video feed requires constant human monitoring. The use of video monitoring is not, therefore, suitable for use with an autonomous system, as is most desired.